Hydrodynamic relationships between gravel pit lakes and aquifers: brief review and insights from numerical investigations

International audienc

Whole mitochondrial genomes unveil the impact of domestication on goat matrilineal variability

Background: The current extensive use of the domestic goat (Capra hircus) is the result of its medium size and high adaptability as multiple breeds. The extent to which its genetic variability was influenced by early domestication practices is largely unknown. A common standard by which to analyze maternally-inherited variability of livestock species is through complete sequencing of the entire mitogenome (mitochondrial DNA, mtDNA). Results: We present the first extensive survey of goat mitogenomic variability based on 84 complete sequences selected from an initial collection of 758 samples that represent 60 different breeds of C. hircus, as well as its wild sister species, bezoar (Capra aegagrus) from Iran. Our phylogenetic analyses dated the most recent common ancestor of C. hircus to ~460,000 years (ka) ago and identified five distinctive domestic haplogroups (A, B1, C1a, D1 and G). More than 90 % of goats examined were in haplogroup A. These domestic lineages are predominantly nested within C. aegagrus branches, diverged concomitantly at the interface between the Epipaleolithic and early Neolithic periods, and underwent a dramatic expansion starting from ~12–10 ka ago. Conclusions: Domestic goat mitogenomes descended from a small number of founding haplotypes that underwent domestication after surviving the last glacial maximum in the Near Eastern refuges. All modern haplotypes A probably descended from a single (or at most a few closely related) female C. aegagrus. Zooarchaelogical data indicate that domestication first occurred in Southeastern Anatolia. Goats accompanying the first Neolithic migration waves into the Mediterranean were already characterized by two ancestral A and C variants. The ancient separation of the C branch (~130 ka ago) suggests a genetically distinct population that could have been involved in a second event of domestication. The novel diagnostic mutational motifs defined here, which distinguish wild and domestic haplogroups, could be used to understand phylogenetic relationships among modern breeds and ancient remains and to evaluate whether selection differentially affected mitochondrial genome variants during the development of economically important breeds

Search for eccentric black hole coalescences during the third observing run of LIGO and Virgo

Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass M>70 M⊙) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities 0<e≤0.3 at 0.33 Gpc−3 yr−1 at 90\% confidence level

Ultralight vector dark matter search using data from the KAGRA O3GK run

Among the various candidates for dark matter (DM), ultralight vector DM can be probed by laser interferometric gravitational wave detectors through the measurement of oscillating length changes in the arm cavities. In this context, KAGRA has a unique feature due to differing compositions of its mirrors, enhancing the signal of vector DM in the length change in the auxiliary channels. Here we present the result of a search for U(1)B−L gauge boson DM using the KAGRA data from auxiliary length channels during the first joint observation run together with GEO600. By applying our search pipeline, which takes into account the stochastic nature of ultralight DM, upper bounds on the coupling strength between the U(1)B−L gauge boson and ordinary matter are obtained for a range of DM masses. While our constraints are less stringent than those derived from previous experiments, this study demonstrates the applicability of our method to the lower-mass vector DM search, which is made difficult in this measurement by the short observation time compared to the auto-correlation time scale of DM

Search for gravitational-lensing signatures in the full third observing run of the LIGO-Virgo network

Gravitational lensing by massive objects along the line of sight to the source causes distortions of gravitational wave-signals; such distortions may reveal information about fundamental physics, cosmology and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO--Virgo network. We search for repeated signals from strong lensing by 1) performing targeted searches for subthreshold signals, 2) calculating the degree of overlap amongst the intrinsic parameters and sky location of pairs of signals, 3) comparing the similarities of the spectrograms amongst pairs of signals, and 4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by 1) frequency-independent phase shifts in strongly lensed images, and 2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the non-detection of gravitational-wave lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects

Signatures-and-sensitivity-based multi-criteria variational calibration for distributed hydrological modeling applied to Mediterranean floods

Classical calibration methods in hydrology are commonly performed with a single cost function computed on long time series. Even though the hydrological model has acceptable scores in NSE and KGE, unbalancing problems can still arise between overall score and the model performance for flood events, and particularly flash floods. Enhancing multi-criteria calibration methods with multi-scale signatures to improve distributed flood modeling remains a challenge. In this study, the potential of hydrological signatures computed continuously and at the scale of flood events on long time series, is employed within various multi-criteria calibration approaches to attain a more efficient hydrological model. This work presents an improved and original signature-based calibration approach, implemented in the variational data assimilation algorithm of SMASH (Spatially distributed Modelling and ASsimilation for Hydrology) platform, applied over 141 catchments mostly located in the French Mediterranean region. Several signatures, especially flood event signatures are firstly computed, relying on a proposed automatic hydrograph segmentation algorithm. Suitable signatures for constraining the model are selected based on their global sensitivity analysis to model parameters. Several multi-criteria calibration strategies with the selected signatures are eventually performed, including a multi-objective optimization approach, and a single-objective optimization approach, that transforms the multi-criteria problem into a single-objective function. Note that in the first approach, the proposed technique based on a simple additive weighting method is used to select an optimal solution obtained from a set of non-inferior solutions. The suggested methods show that, for a global calibration, the average relative error in simulating the peak flow has been dropped from about 0.27 to 0.01-0.08 and from about 0.30 to 0.18-0.21 with various multi-criteria optimization strategies, respectively in calibration and temporal validation. For a distributed calibration, while the average NSE (resp. KGE) still slightly decreases from 0.78 (resp. 0.86) to 0.75 (resp. 0.81) in calibration, the quality of simulated peak flow has been enhanced about 1.5 times in average. In particular, the NSE (resp. KGE) calculated solely on 111 flood events which are picked from 23 downstream gauges has been improved from 0.80 (resp. 0.71) up to 0.83 (resp. 0.78) in median. These results have demonstrated the robustness and delicacy of the model constrained by the signatures for enhancing flash flood forecasting systems

Signatures-and-sensitivity-based multi-criteria variational calibration for distributed hydrological modeling applied to Mediterranean floods

Classical calibration methods in hydrology are commonly performed with a single cost function computed on long time series. Even though the hydrological model has acceptable scores in NSE and KGE, unbalancing problems can still arise between overall score and the model performance for flood events, and particularly flash floods. Enhancing multi-criteria calibration methods with multi-scale signatures to improve distributed flood modeling remains a challenge. In this study, the potential of hydrological signatures computed continuously and at the scale of flood events on long time series, is employed within various multi-criteria calibration approaches to attain a more efficient hydrological model. This work presents an improved and original signature-based calibration approach, implemented in the variational data assimilation algorithm of SMASH (Spatially distributed Modelling and ASsimilation for Hydrology) platform, applied over 141 catchments mostly located in the French Mediterranean region. Several signatures, especially flood event signatures are firstly computed, relying on a proposed automatic hydrograph segmentation algorithm. Suitable signatures for constraining the model are selected based on their global sensitivity analysis to model parameters. Several multi-criteria calibration strategies with the selected signatures are eventually performed, including a multi-objective optimization approach, and a single-objective optimization approach, that transforms the multi-criteria problem into a single-objective function. Note that in the first approach, the proposed technique based on a simple additive weighting method is used to select an optimal solution obtained from a set of non-inferior solutions. The suggested methods show that, for a global calibration, the average relative error in simulating the peak flow has been dropped from about 0.27 to 0.01-0.08 and from about 0.30 to 0.18-0.21 with various multi-criteria optimization strategies, respectively in calibration and temporal validation. For a distributed calibration, while the average NSE (resp. KGE) still slightly decreases from 0.78 (resp. 0.86) to 0.75 (resp. 0.81) in calibration, the quality of simulated peak flow has been enhanced about 1.5 times in average. In particular, the NSE (resp. KGE) calculated solely on 111 flood events which are picked from 23 downstream gauges has been improved from 0.80 (resp. 0.71) up to 0.83 (resp. 0.78) in median. These results have demonstrated the robustness and delicacy of the model constrained by the signatures for enhancing flash flood forecasting systems

SMASH -SPATIALLY DISTRIBUTED MODELLING AND ASSIMILATION FOR HYDROLOGY: PYTHON WRAPPING TOWARDS ENHANCED RESEARCH-TO-OPERATIONS TRANSFER

International audienceThe distributed SMASH platform is based on a gridded mesh and on a modular design. On each cell, the model features different hydrological components. Each component offers different modeling options such as snow modules, surface interception, production, transfer and percolation functions. At the grid scale, different routing models are implemented via a cell-to-cell numerical routing scheme. S MASH comes with its numerical adjoint model which is obtained by automatic differentiation with Tapenade. A variational data assimilation algorithm is implemented and helps to calibrate the distributed parameters or evaluate the model states. This algorithm uses the quasi-Newton lbfgs-b descent algorithm and the gradient of the cost function relative to the model parameters and states. This gradient is computed by a run of the adjoint model. T he numerical SMASH platform is a Fortran code. To gain in modularity and facilitate the use of SMASH in the research and engineering communities, a Python interface has been created with the new F90Wrap software. The original Fortran code has been revamped. The new structure enables to 1) control any inputs and outputs with Python, 2) keep an automatically differentiable and computationally efficient numerical Fortran model, 3) call a binary from the shell to preserve a backward compatibility with old practices. T he key to achieve this Python interface is to use Fortran modules and derived types to store all inputs and outputs variables. These Fortran structures are stored in different modules. F 90Wrap automatically generates the fortran functions and wrappers to give access to every component of each derived type. A Python class is generated to facilitate the use of these wrappers inside a Python code. T he Python object "model" aggregates all inputs/outputs variables required by SMASH. The "model" object comes with built-in methods to allow end users to perform simulations, calibrations, plotting and hdf5 export. The Python binding facilitates all post-processing since it does not requires I/O into text files anymore